Abstract
The non-destructive method for detection of fungal contamination in peach fruit using hyperspectral imaging was evaluated. Growth characteristics of three major spoilage fungi in peach fruit during decay were estimated. Three quantitative prediction models were then constructed to forecast the microbial content from the HSI datasets. The prediction of fungal contamination on the fruit was visualized with different colors. Additionally, principal component analysis (PCA) was applied to reduce the dimensionality of the HSI data and to discriminate the infection degree in peaches. The results showed that partial least squares regression (PLSR) could achieve performance with Rp2 not less than 0.84in predicting fungal colony counts, while PCA scores successfully identified the infected degrees of samples. This study illustrates that HSI combined with chemometrics can potentially be implemented for the quantitative detection of fungal contamination in peach fruit.
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Acknowledgments
The authors would like to thank the National Natural Science Foundation of China (NSFC: 31671925; 31671926) for financial support and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the 2017’ Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0631).
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Qiang Liu declares that he has no conflict of interest. Dandan Zhou declares that she has no conflict of interest. Siying Tu declares that she has no conflict of interest. Hui Xiao declares that she has no conflict of interest. Bin Zhang declares that he has no conflict of interest. Ye Sun declares that she has no conflict of interest. Leiqing Pan declares that he has no conflict of interest. Kang Tu declares that he has no conflict of interest.
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Liu, Q., Zhou, D., Tu, S. et al. Quantitative Visualization of Fungal Contamination in Peach Fruit Using Hyperspectral Imaging. Food Anal. Methods 13, 1262–1270 (2020). https://doi.org/10.1007/s12161-020-01747-x
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DOI: https://doi.org/10.1007/s12161-020-01747-x